Our home turf is the automated understanding and processing of texts also known as natural language processing, text analytics or text mining

Our approach far exceeds traditional text analytics and is not comparable to common text-analytics APIs say from Google, Microsoft, Amazon, IBM or similar vendors.

Traditional text analytics

Traditional text analytics used named entity recognition and typically simple heuristics based on keywords or keyword combinations to find the correct relations between the entities mentioned in this text. However, for complex sentences both accuracy and coverage go down.

Machine learning

Modern approaches often rely heavily on machine learning. Machine learning requires clean training data that contains the desired output labels. This is typically not available.It is usually not possible to explain why a machine learning system derived a certain result. This results in less transparency.

AI

Artificial intelligence (= AI) is a major buzzword today. In many cases, it is equated with machine learning. With the advancements of neural networks and deep learning over recent years, amazing results have been achieved especially in the field of image recognition and generation of texts and images.

By conducting a deep linguistic analysis of the underlying predicate-argument structure, Glanos can handle real-life complex sentences in a robust and predictable manner. Machine-learning is used to build up and enrich semantic resources ahead of time so that a specific training set from a client is usually only needed for final adjustments.

Glanos does not only recognize entities in form of strings, but rather as references to real-world persons, companies and locations.